1. 配合视频服用更佳:
https://www.bilibili.com/video/BV16i4y1x7Yn/
2. 视频中使用到的命令:
mkdir /home/aistudio/cuda10 mkdir /home/aistudio/cuda92 mkdir /home/aistudio/cudnn mkdir /home/aistudio/tf cp /home/aistudio/data/data32924/cudnn-9.2.tgz /home/aistudio/cudnn cp /home/aistudio/data/data32924/cudnn-10.1.tgz /home/aistudio/cudnn pip install torch==1.5.0+cu101 torchvision==0.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html -t /home/aistudio/cuda10 pip install torch==1.5.0+cu92 torchvision==0.6.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html -t /home/aistudio/cuda92 pip install tensorflow-gpu==1.15 -t /home/aistudio/tf tar -zxvf cudnn-9.2.tgz tar -zxvf cudnn-10.1.tgz import sys sys.path.append('/home/aistudio/cuda92') sys.path.append('/home/aistudio/tf') import torch import tensorflow as tf print(torch.cuda.is_available()) print(tf.test.is_built_with_cuda())
Pytorch的Helloworld
import os import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms BATCH_SIZE=512 #大概需要2G的显存 EPOCHS=20 # 总共训练批次 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 让torch判断是否使用GPU,建议使用GPU环境,因为会快很多 MODEL_PATH="model/helloworld.pth" os.environ['CUDA_VISIBLE_DEVICES'] = '3' train_loader = torch.utils.data.DataLoader( datasets.MNIST('data', train=True, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=BATCH_SIZE, shuffle=True) test_loader = torch.utils.data.DataLoader( datasets.MNIST('data', train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=BATCH_SIZE, shuffle=True) # 下面我们定义一个网络,网络包含两个卷积层,conv1和conv2,然后紧接着两个线性层作为输出, # 最后输出10个维度,这10个维度我们作为0-9的标识来确定识别出的是那个数字 class ConvNet(nn.Module): def __init__(self): super().__init__() # batch*1*28*28(每次会送入batch个样本,输入通道数1(黑白图像),图像分辨率是28x28) # 下面的卷积层Conv2d的第一个参数指输入通道数,第二个参数指输出通道数,第三个参数指卷积核的大小 self.conv1 = nn.Conv2d(1, 10, 5) # 输入通道数1,输出通道数10,核的大小5 self.conv2 = nn.Conv2d(10, 20, 3) # 输入通道数10,输出通道数20,核的大小3 # 下面的全连接层Linear的第一个参数指输入通道数,第二个参数指输出通道数 self.fc1 = nn.Linear(20*10*10, 500) # 输入通道数是2000,输出通道数是500 self.fc2 = nn.Linear(500, 10) # 输入通道数是500,输出通道数是10,即10分类 def forward(self,x): in_size = x.size(0) # 在本例中in_size=512,也就是BATCH_SIZE的值。输入的x可以看成是512*1*28*28的张量。 out = self.conv1(x) # batch*1*28*28 -> batch*10*24*24(28x28的图像经过一次核为5x5的卷积,输出变为24x24) out = F.relu(out) # batch*10*24*24(激活函数ReLU不改变形状)) out = F.max_pool2d(out, 2, 2) # batch*10*24*24 -> batch*10*12*12(2*2的池化层会减半) out = self.conv2(out) # batch*10*12*12 -> batch*20*10*10(再卷积一次,核的大小是3) out = F.relu(out) # batch*20*10*10 out = out.view(in_size, -1) # batch*20*10*10 -> batch*2000(out的第二维是-1,说明是自动推算,本例中第二维是20*10*10) out = self.fc1(out) # batch*2000 -> batch*500 out = F.relu(out) # batch*500 out = self.fc2(out) # batch*500 -> batch*10 out = F.log_softmax(out, dim=1) # 计算log(softmax(x)) return out model = ConvNet().to(DEVICE) optimizer = optim.Adam(model.parameters()) def train(model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if(batch_idx+1)%30 == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) torch.save(model, MODEL_PATH) def test(model, device, test_loader): # model.eval() # 直接导入也可 # 以下为通过路径读 model=None model=torch.load(MODEL_PATH) model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # 将一批的损失相加 pred = output.max(1, keepdim=True)[1] # 找到概率最大的下标 correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) for epoch in range(1, EPOCHS + 1): train(model, DEVICE, train_loader, optimizer, epoch) test(model, DEVICE, test_loader)
Tensorflow Hello World程序
import tensorflow as tf # 创建常量 hello = tf.constant('Hello,world!') # 创建会话 sess = tf.Session() # 执行 result = sess.run(hello) # 关闭会话 sess.close() # 输出结果 print(result)